In running day-to-day business, many companies use database systems to collect large amounts of data. Data mining techniques have been developed to help these companies to effectively manage the data, and extract insightful information therefrom to improve their business. For example, in a service or retail business, a favorable promotional period may be determined by applying one such data mining technique to sales data collected over time.
Data mining techniques normally use association rules to determine correlations between attributes of the collected data. Each rule has support and confidence measures associated therewith. In accordance with one such technique, a problem of identifying, say, a favorable promotional period may be formulated using an optimized association rule containing at least one uninstantiated attribute, namely, an unknown time interval for the promotional period. In solving the problem, the attribute is instantiated, i.e., the time interval is identified, such that either the support or confidence of the optimized association rule is maximized.
In prior art, application of an optimized association rule to determine an optimal interval is described in T. Fukuda et al., "Mining Optimized Association Rules for Numeric Attributes," Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Jun. 1996, pp. 182-191. However, in this prior art application, only a single optimal interval can be determined, which is inadequate in many applications where multiple favorable intervals are required. The deficiency of the prior art technique may stem from the general belief that fulfillment of an optimized association rule containing an arbitrary number of uninstantiated attributes by exhausting all possible instantiations therefor based on a large amount of data is impractical.
Accordingly, in data mining where an optimized association rule containing a multiplicity of uninstantiated attributes is used, a methodology for effectively instantiating such attributes is needed.